A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting

Abstract In the milling process, chatter, which results in poor surface quality, dimensional errors, reduced cutter and machine life, is one of the main limitations on performance. In this paper, a novel method of online chatter detection for milling processes is developed. In this method, first, the spindle revolution period component is obtained via angular synchronous averaging (ASA). Then, the residual part related to chatter information is calculated by subtracting the periodic component. Secondly, the multiscale permutation entropy (MPE) and multiscale power spectral entropy (MPSE) of the residual part are calculated, and the Laplacian score (LS) for feature selection is applied to select the optimal sensitive scale features with generalization. Online chatter detection that is based on selected sensitive scale features by splitting signal up into (overlapping) frames in milling process. Finally, a trained gradient tree boosting (GTB) can be used to intelligent diagnosis of the chatter severity level. The analysis results show that the proposed method can effectively detect the onset of chatter under stable cutting conditions and variable cutting conditions, which is simpler and more robust than the existing methods.